Numerous methodologies exist to assist in providing a machine translation of natural language content from the language of the content to another language. One common approach in machine translation applies rules-based methodologies. Rules-based methodologies rely on bilingual dictionaries, as well as linguistic information about the source and target languages, such as grammar rules and sentence structure. A simple rules-based approach might translate each word of the source language content into the corresponding word of the target language, and then rearrange, as needed, the target language words to comply with the grammar rules of the target language. Another common approach in machine translation uses statistical-based methodologies. This approach typically is a string- or phrase-based approach, and applies a probability distribution to a potential translation to find the most likely translation of a word based on, for example, the previous word or words that have been translated. These approaches can produce high quality translations. However, the source and target natural languages need to be identified. The target natural language is typically identified by the system user requesting the translation. The source language is usually identified by the system user requesting the translation, but can be identified by machine, usually through a dictionary look-up operation, if the source language content sufficient.